Spectral analysis for sampling image-based rendering data

摘要

Image-based rendering (IBR) has become a very active research area in recent years. The spectral analysis problem for IBR has not been completely solved. In this paper, we present a new method to parameterize the problem, which is applicable for general-purpose IBR spectral analysis. We notice that any plenoptic function is generated by light ray emitted/reflected/refracted from the object surface. We introduce the surface plenoptic function (SPF), which represents the light rays starting from the object surface. Given that radiance along a light ray does not change unless the light ray is blocked, SPF reduces the dimension of the original plenoptic function to 6D. We are then able to map or transform the SPF to IBR representations captured along any camera trajectory. Assuming some properties on the SPF, we can analyze the properties of IBR for generic scenes such as scenes with Lambertian or non-Lambertian surfaces and scenes with or without occlusions, and for different sampling strategies such as lightfield/concentric mosaic. We find that in most cases, even though the SPF may be band-limited, the frequency spectrum of IBR is not band-limited. We show that non-Lambertian reflections, depth variations and occlusions can all broaden the spectrum, with the latter two being more significant. SPF is defined for scenes with known geometry. When the geometry is unknown, spectral analysis is still possible. We show that with the "truncating windows" analysis and some conclusions obtained with SPF, the spectrum expansion caused by non-Lambertian reflections and occlusions can be quantatively estimated, even when the scene geometry is not explicitly known. Given the spectrum of IBR, we also study how to sample IBR data more efficiently. Our analysis is based on the generalized periodic sampling theory with arbitrary geometry. We show that the sampling efficiency can be up to twice of that when we use rectangular sampling. The advantages and disadvantages of generalized periodic sampling for IBR are also discussed.